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Record W4411715811 · doi:10.1093/geront/gnaf155

Unpacking the dynamics of transitional care units in Ontario, Canada

2025· article· en· W4411715811 on OpenAlexafffundabout
Alexandra Krassikova, Lydia Yeung, Jennifer Bethell, Martine Puts, Sandra McKay, Sara J. T. Guilcher, Kerry Kuluski, Katherine S. McGilton

Bibliographic record

VenueThe Gerontologist · 2025
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsInstitute for Work & HealthTrillium Health CentreUniversity of TorontoToronto Rehabilitation InstituteUniversity Health Network
FundersCanadian Institutes of Health ResearchTrillium Health Partners FoundationUniversity of Toronto
KeywordsThematic analysisPsychosocialTransitional careUnpackingExploratory researchPsychologyQualitative researchNursingHealth careMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Transitional care units (TCUs) provide short-term, low-intensity, restorative care to patients who are medically stable but unable to leave the hospital due to factors, such as lack of support. In Ontario, Canada, TCUs have been implemented over the past decade, yet little is known about their operation. This study aimed to explore the structural characteristics and the care processes of TCUs from the perspective of TCU managers. RESEARCH DESIGN AND METHODS: An exploratory descriptive qualitative design was employed. Semi-structured interviews were conducted with seven TCU managers. A five-step inductive thematic analysis was used to identify themes. Participants' median age was 46 years (range 36-50), with four men and three women. Their experience as TCU managers at the time of the interview ranged from 2 months to 5 years. RESULTS: The study results suggest variation across TCUs in terms of structure and patient populations served. Four themes were identified related to the care processes across seven TCUs: (1) ensuring safe transitions; (2) managing patients' expectations; (3) creating a team that works together; and (4) navigating a constantly changing environment. DISCUSSION AND IMPLICATIONS: Taking into account the variability of models, implementation and evaluation of these programs require careful planning. The complex medical and psychosocial needs of TCU patients should be considered when designing these units to ensure effective and appropriate care delivery.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.095
GPT teacher head0.361
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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